Bibliographic record
Abstract
With 2009 the Year of Homecoming in Scotland, it seems appropriate to take stock of the state of Scottish Studies in one part of the New World.What follows is a snapshot of teaching and research in post-secondary institutions across Canada, showing a diverse and robust group of researchers.A survey was sent out in Spring of this year, and the discussion here is largely based on the results of the thirty-seven responses we received to the survey, although we have also supplemented it with some additional information, identifying fifty people in all who work primarily or partly in Scottish Studies.We may have missed some researchers, but we hope this article will give some idea of the types of Scottish humanities research being carried out in Canada.Those with Scottish interests are to be found across the country, in almost every province.A cross-country survey, province by province, will be found below.One very positive result of the survey is the number of people with Scottish research interests who have joined Canadian universities since 2000.Sixteen of the respondents began their current position in the last decade, and the actual number may be higher when those who did not respond are taken into account.Graduates of Scottish Studies are now widely dispersed; we know of several who have gone on to Ewan & Parker IRSS 34 (2009) 139
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.026 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".